What changed? Why did it change? Business leaders in companies of all sizes and industries ask those questions daily as they try to make sense of current results and determine decisions for the future. Variance analysis is a crucial tool for answering those questions. Also called flux analysis, variance analysis compares two pieces of data and displays the quantitative difference between them, as well as the underlying reason — think budget versus actuals or current year compared to prior year. It should be performed regularly, but some organizations let it slide because it can be tedious and time-consuming. Here’s what business leaders need to know about variance analysis, including how to do it and why you shouldn’t let it slip.
What Is Variance Analysis?
Variance analysis is an exercise that businesses use to explain changes in financial data. It can be the comparison of any two points — commonly, comparing current actual results to a benchmark, such as a budget, forecast or prior period. Its goal is to quantify the degree of change and provide a comprehensive explanation, which it does through a combination of numeric and textual descriptions. Information provided by variance analysis helps company leaders understand what is going on in the business, relative to the expectations established by the benchmark, so they can decide whether operational course corrections are needed. In this way, it informs future business decisions and helps companies build flexible budgeting processes.
In addition, variance analysis is a useful tool for uncovering accounting errors or omissions and can raise a red flag on unusual activity that might indicate fraud. Through examination of fluctuations, variance analysis acts as a detective control, similar to account reconciliations. For this reason, auditors often begin and end an audit with a variance analysis as a “reasonableness test” — a high-level review to identify anything that looks out of whack. When used in this way, the desired variance analysis would be between current actual financials and a prior period’s audited financials.
Key Takeaways
- Variance analysis compares two sets of financial data and provides quantitative and qualitative explanations for any differences.
- It’s a practical tool for managing current operations and informing future plans, as well as an effective detective control to flag errors and anomalies in financial information.
- Variances are often the result of changes in the volume, cost or efficiency of an activity or in any combination of the three.
- Capturing high-quality data in accounting software helps make variance analysis less time-consuming and timelier, and it facilitates richer, more actionable explanations.
Variance (Flux) Analysis Explained
Most often, variance analysis is performed at the end of a financial close to compare the actual results for a period against another benchmark, such as the budget. It can be applied to all levels of financial information — broadly, to financial statements, and granularly, to general ledger accounts. For example, variance analysis can be prepared for the total revenue line on an income statement to compare current period actuals to the budgeted levels. Or it can be done for one particular revenue account to understand the changes happening to, say, a product, assessing specific factors, such as volume sold and price, as well as related changes, like the rate of returns. Variance analysis usually is performed on all three of the major financial statements: the balance sheet, income statement and cash flow statement. It can focus on any of the components of those statements, such as assets, liabilities, revenue, sales and cash flow.
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Variance Analysis Formula
Variances are calculated to show dollar difference and the related percentage of change. While there are many possible types of variances, the variance formulas are straightforward and always the same. Dollar variance is simply the current, or new, amount for the data in question minus the “old” value for the same data (usually either from the prior period or against a benchmark, such as the budget or forecast). A common example is current-year actual minus the prior-year actual. The percentage change calculation can be thought of as “new minus old divided by old.” When performing a variance analysis of a current year balance versus the prior year, the formulas would look like this:
Dollar Variance = Current Year – Prior Year
Percentage Change = (Current Year – Prior Year) / Prior Year
By convention, variable analyses typically show negative variances in red and positive variances in black. Additionally, negative variances may be shown in parentheses and positive variances without. To demonstrate the formulas in action using a hypothetical example, consider SAR Sporting Goods, a fictional soccer equipment retailer, which generated $100,000 in actual sales for the month of February 2023. Budgeted sales in February 2022 were $90,000 and actual sales for the prior February were $120,000. The high-level variance analysis of overall sales is shown below. Variance explanations (a) and (b) shed additional light on the quantitative changes.
SAR Sporting Goods
Variance Analysis
February 2023
| February 2023 Actual |
February 2023 Budget |
February 2022 Actual |
Budget vs. Actual
$ % |
Prior Year vs. Actual
$ % |
|||||
|---|---|---|---|---|---|---|---|---|---|
| Sales | $ 100,000 | $ 90,000 | $ 120,000 | $ 10,000 | 11% (a) | $ (20,000) | 17%(b) | ||
Most Common Types of Variances
Variances are everywhere in a business’s financial data. Income statement data tends to be a common starting point for variance analysis, focusing on fluctuations in sales and expenses. Unexpected differences in sales from a business’s budgeted expectations might come from external causes, like shrinking markets or increased consumer demand, or might indicate problems in the sales team or distribution channels. For these reasons, it’s important for variance investigations to also consider operational data alongside the accounting information, which is always good practice in financial management.
Beyond the income statement, valuable information can be revealed by analyzing changes in balance sheet accounts. Fluctuations in accounts like inventory, accounts receivable and accounts payable, for example, might represent cash flow problems like “hidden” sources and uses of cash, so it’s important to understand what’s causing those fluctuations.
The most common types of variances include:
- Revenue: These relate to fluctuations in sales compared to past periods or expectations. It’s useful to understand why revenue is higher or lower than in prior periods, budgets and forecasts.
- Expense: Changes in costs, compared to other periods and expectations, are important to analyze carefully and in the context of other fluctuations. Not all increases in costs are bad, and not all decreases are good. For example, the cost of goods sold can be lower than expected because of lower cost of materials or because the number of products sold for the period was lower.
- Labor: This expense variance is specific to payroll cost, including hourly, salaried and contract workers. When labor costs are lower than those in the comparison point, it’s considered a positive variance. When labor costs are higher than expected, or than in a prior year, it’s generally considered negative. As with all expenses, however, it’s important to understand the underlying causes to determine whether the change is good or bad.
- Material: This expense variance is specific to the cost of materials needed to produce products for sale. It includes both direct material costs, such as raw materials like timber, as well as some indirect costs, like gloves for factory workers and the transportation costs of obtaining the materials. When material costs are less than the comparison point, it’s thought to be a positive variance. When material costs are higher than expected, or than in a prior year, it’s generally considered a negative variance. Again, it’s important to understand the underlying causes.
- Overhead: Overhead refers to indirect costs that a business incurs outside the direct costs of producing a product or service. A few examples are utilities, corporate staff salaries, legal fees and interest expenses, but there are many types of expenses that can be classified as overhead. An overhead variance is a specific expense variance that relates to the difference between actual overhead costs and those included in the comparison point. When actual overhead is lower than its comparison value, it’s considered positive; when it’s higher, it’s seen as negative.
Typical Interpretations of the Most Common Types of Variances
| Variance Type | Actual > Benchmark | Actual < Benchmark |
|---|---|---|
| Revenue | Positive | Negative |
| Expenses | Negative | Positive |
| Labor | Negative | Positive |
| Material | Negative | Positive |
| Overhead | Negative | Positive |
Reasons for Variance
There are two broad reasons why variances arise in fluctuation analyses. The first is errors in the data, such as omissions, data-entry mistakes and incorrect general ledger coding. Variances caused by data errors can be fixed by the accounting staff and that’s that — they don’t represent true business variances. That said, they may indicate an issue within the business’s accounting processes, which should be addressed if they’re recurring. The second reason variances arise is due to real changes in operations relative to the benchmark. These are the variances that business leaders must spend more time thinking through.
Often, the real business change illuminated by the variance comes from fluctuations in volume, price or efficiency:
- Volume variances arise when a core element within a transaction changes. Examples include selling more products than expected, purchasing greater amounts of raw materials or incurring more labor hours. Volume variances can be positive or negative, depending on the specific situation.
- Price variances occur when the value of a business activity differs from the expected value. For example, higher prices for raw materials can create a negative price variance. On the other hand, an increased selling price for a product or reduced use of product discounts will result in positive price variances.
- Efficiency variances can be particularly useful to examine. These variances arise from changes in consumption or usage in operations. If a product was expected to take 10 labor hours to complete but instead was finished in 9.5 labor hours, that shows up as a positive efficiency variance.
Each of these variance types can result from a change in volume, price or efficiency — but it’s common for fluctuations to be caused by a combination of all three. Consider a hypothetical manufacturer of table linens that had a small variance in its raw materials expense account for the month of May 2023, as compared to its expenses for May 2022. Upon investigation, a finance manager identified several reasons that caused the current-year expense to be higher than that for the prior year — though not as high as expected. Due to increased forecasted sales, the company purchased more fabric than it had in past years, creating a volume variance. And due to supply chain issues, the cost per yard of the fabric also rose significantly, creating a price variance.
However, as a result of a first-quarter 2023 investment in innovative sewing and cutting equipment that reduced material waste, linen yardage per tablecloth fell by 25%. This positive efficiency variance partially offset the negative volume and price variances, reducing the impact of the overall increase in raw materials. Because each of the component variances were investigated, company management ended up with a better understanding of both the impact of increasing price trends and the positive return on the equipment purchase, allowing managers to adjust forecasts accordingly.
Issues With Variance Analysis
Variance analysis is a useful accounting tool for managing business operations, as well as an internal control to detect irregularities. But even though performing regular variance analyses helps keep the task manageable and timely, a handful of potential issues sometimes prompts an organization to skimp on them:
- Lack of business acumen. The most useful variance analyses are those that fully investigate the variances and convey the contributing reasons in an easy-to-understand way, but this can’t be accomplished without a strong understanding of the business. The task requires analysts who understand the interdependencies among accounts, such as the relationships among revenue, inventory and cost of goods sold. The analyst must also be familiar with company operations in order to differentiate the change drivers from their consequences.
- Required data. High-quality variance analysis relies on complete, accurate data — something not all companies produce. It also requires access to both accounting information and operational data. The most useful variance explanations are based on up-to-date information that is trusted by the organization’s stakeholders.
- Unclear policies. Many organizations never establish clear policies about when to perform variance analyses or how to do them. At a minimum, it should be a routine step in the monthly closing process. In addition, ad hoc fluctuation analyses should be prepared whenever an important change is spotted. The policy should establish materiality levels that tell analysts what changes are important, which accounts to analyze and how to prioritize specific variance analyses, based on what makes sense for the business. A materiality level can indicate how thoroughly an analyst should investigate a variance, so as to avoid spending time researching small, immaterial changes. The policy should also assign responsibility and set deadlines to ensure that variance analyses happen.
NetSuite Makes Variance Analysis Better, Stronger and Faster
Good financial software solutions, such as NetSuite Planning and Budgeting, can provide the timely information needed to prepare accurate variance analyses faster and easier. NetSuite Planning and Budgeting delivers high-quality, up-to-date financial and operational information because it shares a unified data repository with NetSuite Enterprise Resource Planning (ERP). The ability to view data at many levels, drilling down and zooming out, makes investigating variances easier and faster. And because answering questions about one variance might raise others, NetSuite’s flexible reporting features can be a big time-saver by enabling changes, such as the range of accounts, time period or the benchmark, without having to re-create the wheel every time.
Variance analysis is a helpful tool for understanding where changes are happening in a business and the reasons behind them. Comparing actuals with expected and historical results can uncover business changes that are areas of opportunity or places in need of improvement. Variance analysis is most useful when financial analysts fully investigate the underlying causes of variances and write thorough explanations powered by high-quality financial and operational data. Further, because variance analysis is also a useful internal control for catching anomalies, it’s important that companies do it regularly. The right software can reduce the resources and time required to perform variance analysis and get powerful information in the right hands more quickly.
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Variance Analysis FAQs
What does the analysis of variance tell us?
The analysis of a variance explains the amount of the fluctuation and the reasons why things changed. Most variances are due to changes in the volume, price or efficiency of an activity, or a combination of the three. Sometimes variance analysis uncovers data errors and can be a useful internal control.
What is flux analysis?
Flux analysis is a casual name for fluctuation analysis, which, in accounting and finance language, is interchangeable with variance analysis. Flux analysis compares two pieces of financial data — say, this month’s sales and last month’s sales — and then answers the questions “What changed?” and “Why?”
How do you write a flux analysis?
A flux (or variance) analysis is commonly set up with columns that, from left to right, show two data points being compared, along with their dollar difference and the percentage of change between them. Common formatting conventions show negative variances in red and/or inside parentheses and positive variances in black without parentheses.
Is flux analysis the same as variance analysis?
Yes. In the realm of accounting and finance, flux analysis is the same as variance analysis. Flux analysis has different meanings in other disciplines.
What is fluctuation analysis accounting?
Fluctuation analysis in accounting is the investigation of changes in two financial data points. Most often, actual results are compared to budgets, forecasts or other time periods. The purpose is to explain why the changes occurred so as to better inform future decisions.
What is variance in analysis?
Variance analysis is a comparison of data that identifies changes and tells us the reason why. It requires the ability to identify the causes and effects of changes, as well as the relationships among various accounts. It helps business leaders better understand actual business results.
What is an example of variance analysis?
A quite common example of variance analysis is the comparison of actual results to budgeted results for a certain period. This comparison can highlight areas that didn’t go as planned and explain why. Sometimes the reasons are positive, while other times they are negative.
What are the methods of variance analysis?
Variance analysis can be approached broadly at the financial statement level, comparing actual income statements, balance sheets and cash flow statements with their budgeted, forecasted or prior-period counterparts. Another method of variance analysis involves concentrating on changes in specific general ledger accounts, such as product revenue, cost of goods sold or interest expense.
What is the objective of variance analysis?
The objective of variance analysis is to identify areas of a business that have changed and explain the changes’ underlying causes. The changes can be relative to planned activity or other time periods. By identifying changes, investigating them and providing useful, data-supported explanations, managers can get a better understanding of business and operational results to use when making future decisions.
